1. Field of the Invention
The present invention pertains to a method and apparatus for pattern recognition, and in particular to a system which provides an indication of the confidence with which a candidate is selected for an unknown pattern.
2. Description of the Related Art
In a pattern recognition system, an unknown pattern, for example, a pattern derived from an image scanner or received by facsimile transmission, is analyzed to determine the identity of the pattern. FIG. 15 illustrates a typical pattern recognition process which identifies individual characters in an image which contains plural such characters.
As shown in FIG. 15, an image, derived for example, from an image scanner, is input in step S1501 and the individual characters in the image are segmented in step S1502. Steps S1501 and S1502 are typically performed by a general purpose host computer which then transmits the characters segmented in step S1502 to a dedicated optical character recognition device. In step S1503, the optical character recognition device subjects the segmented characters to feature extraction whereby a feature vector is derived for the unknown pattern. The feature vector represents various features concerning the pattern such as stroke position, direction, length, and so forth. Then, in rough classification step 1504, the feature vector for the unknown pattern is then compared to a dictionary which contains plural feature vectors for standard patterns. Specifically, the feature vector for the unknown pattern is compared with each feature vector in the standard pattern dictionary and a distance value is calculated representing the mathematical distance between the feature vector for the unknown pattern and the feature vector for the standard pattern. The distance values are sorted and the best candidates, for example, the 52 candidates that are nearest to the feature vector for the unknown pattern, are selected. In step S1505, the best candidates are subjected to detailed classification. In detail classification, additional discriminant functions, for example a pseudo-bayesian discriminant function, are employed to select the best candidate or candidates from among those determined in the rough classification step. The best candidate or candidates for the unknown pattern are then transmitted back to the general purpose host computer where they are subjected to post processing in step S1506. Post processing typically entails processing such as spell-checking, context checking and syntax processing and results in selection of one candidate for the unknown pattern.
While the process depicted in FIG. 15 permits identification of candidates for unknown patterns with high accuracy, there still remains a problem. Specifically, situations still occur in which the unknown pattern cannot be identified with a high degree of confidence and even the best candidate is of questionable accuracy.